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https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13209422#comment-13209422
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Christian Herta edited comment on MAHOUT-976 at 2/16/12 3:11 PM:
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new patch available:

 - Basis implementation 
 - simple tests for propagation and learning by backprop
                
      was (Author: chrisberlin):
    16. Feb. 2012
some tests of learning
                  
> Implement Multilayer Perceptron
> -------------------------------
>
>                 Key: MAHOUT-976
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-976
>             Project: Mahout
>          Issue Type: New Feature
>    Affects Versions: 0.7
>            Reporter: Christian Herta
>            Priority: Minor
>              Labels: multilayer, networks, neural, perceptron
>         Attachments: MAHOUT-976.patch, MAHOUT-976.patch
>
>   Original Estimate: 80h
>  Remaining Estimate: 80h
>
> Implement a multi layer perceptron
>  * via Matrix Multiplication
>  * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: 
> "Efficent Backprop"
>  * arbitrary number of hidden layers (also 0  - just the linear model)
>  * connection between proximate layers only 
>  * different cost and activation functions (different activation function in 
> each layer) 
>  * test of backprop by gradient checking 
>  * normalization of the inputs (storeable) as part of the model
>  
> First:
>  * implementation "stocastic gradient descent" like gradient machine
>  * simple gradient descent incl. momentum
> Later (new jira issues):  
>  * Distributed Batch learning (see below)  
>  * "Stacked (Denoising) Autoencoder" - Feature Learning
>  * advanced cost minimazation like 2nd order methods, conjugate gradient etc.
> Distribution of learning can be done by (batch learning):
>  1 Partioning of the data in x chunks 
>  2 Learning the weight changes as matrices in each chunk
>  3 Combining the matrixes and update of the weights - back to 2
> Maybe this procedure can be done with random parts of the chunks (distributed 
> quasi online learning). 
> Batch learning with delta-bar-delta heuristics for adapting the learning 
> rates.    
>  

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